eprintid: 10044023
rev_number: 27
eprint_status: archive
userid: 608
dir: disk0/10/04/40/23
datestamp: 2018-02-23 16:17:43
lastmod: 2021-09-26 23:04:44
status_changed: 2018-02-23 16:17:43
type: article
metadata_visibility: show
creators_name: Taylor, PN
creators_name: Sinha, N
creators_name: Wang, Y
creators_name: Vos, SB
creators_name: de Tisi, J
creators_name: Miserocchi, A
creators_name: McEvoy, AW
creators_name: Winston, GP
creators_name: Duncan, JS
title: The impact of epilepsy surgery on the structural connectome and its relation to outcome
ispublished: inpress
subjects: UCH
divisions: UCL
divisions: B02
divisions: C07
divisions: D07
divisions: F81
divisions: B04
divisions: C05
divisions: F48
keywords: Connectome, Network, Temporal lobe epilepsy, Surgery, Machine learning, Support vector machine (SVM)
note: © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
abstract: BACKGROUND: 
Temporal lobe surgical resection brings seizure remission in up to 80% of patients, with long-term complete seizure freedom in 41%. However, it is unclear how surgery impacts on the structural white matter network, and how the network changes relate to seizure outcome.

METHODS: 
We used white matter fibre tractography on preoperative diffusion MRI to generate a structural white matter network, and postoperative T1-weighted MRI to retrospectively infer the impact of surgical resection on this network. We then applied graph theory and machine learning to investigate the properties of change between the preoperative and predicted postoperative networks.


RESULTS: 
Temporal lobe surgery had a modest impact on global network efficiency, despite the disruption caused. This was due to alternative shortest paths in the network leading to widespread increases in betweenness centrality post-surgery. Measurements of network change could retrospectively predict seizure outcomes with 79% accuracy and 65% specificity, which is twice as high as the empirical distribution. Fifteen connections which changed due to surgery were identified as useful for prediction of outcome, eight of which connected to the ipsilateral temporal pole.

CONCLUSIONS: 
Our results suggest that the use of network change metrics may have clinical value for predicting seizure outcome. This approach could be used to prospectively predict outcomes given a suggested resection mask using preoperative data only.
date: 2018-01-31
date_type: published
official_url: https://doi.org/10.1016/j.nicl.2018.01.028
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
article_type_text: Journal Article
verified: verified_manual
elements_id: 1535740
doi: 10.1016/j.nicl.2018.01.028
lyricists_name: de Tisi, Jane
lyricists_name: Duncan, John
lyricists_name: McEvoy, Andrew
lyricists_name: Miserocchi, Anna
lyricists_name: Vos, Sjoerd
lyricists_name: Winston, Gavin
lyricists_id: JDETI66
lyricists_id: JSDUN52
lyricists_id: AWMCE45
lyricists_id: AMISE50
lyricists_id: SVOSX19
lyricists_id: GWINS71
actors_name: Smith, Daniel
actors_id: DSMIT53
actors_role: owner
full_text_status: public
publication: NeuroImage: Clinical
volume: 18
pagerange: 202-214
issn: 2213-1582
citation:        Taylor, PN;    Sinha, N;    Wang, Y;    Vos, SB;    de Tisi, J;    Miserocchi, A;    McEvoy, AW;         ... Duncan, JS; + view all <#>        Taylor, PN;  Sinha, N;  Wang, Y;  Vos, SB;  de Tisi, J;  Miserocchi, A;  McEvoy, AW;  Winston, GP;  Duncan, JS;   - view fewer <#>    (2018)    The impact of epilepsy surgery on the structural connectome and its relation to outcome.                   NeuroImage: Clinical , 18    pp. 202-214.    10.1016/j.nicl.2018.01.028 <https://doi.org/10.1016/j.nicl.2018.01.028>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10044023/1/1-s2.0-S2213158218300287-main%283%29.pdf